Image compression schemes reduce the amount of data used to represent a given quantity of information. Preferably, this reduction is based on redundant data in the image, and deleterious effects on the image are minimized.
For example, S3 texture compression (also known as DXTn or DXTC) refers to a group of fixed-rate image compression algorithms which are well-suited for compressing textures in hardware-accelerated 3D computer graphics. In one type of DXT compression, images are divided into 4×4 pixel blocks, or “texels”. For each texel, two color values are chosen to represent the range of pixel colors within that block, and each pixel is mapped to one of four possible colors (two bits) within that range. The compressed texel is represented by the two 16-bit color values, and 16 2-bit pixel values, totaling 64 bits of data per texel, amounting to an overall image size of 4 bits per pixel. However, such compression varieties are not dependent on the image compressed, and thus compress all portions of the image without regard to local variations, i.e., details, in the image.
Some attempts have been made to ameliorate the situation. For example, DiVerdi, Candussi, and Höllerer of the University of California, Santa Barbara, have presented a method for using large, high dimension and high dynamic range datasets on modern graphics hardware, including processing datasets with a discrete wavelet transform and storing the same in a 2D texture memory.